We
introduce a new heterogeneous CPU+GPU-enhanced DFTB approach for the routine
and efficient simulation of large chemical and biological systems. Compared to
homogenous computing with conventional CPUs, heterogeneous computing approaches
exhibit substantial performance with only a modest increase in power
consumption, both of which are essential to upcoming exascale computing
initiatives. We show that DFTB-based molecular dynamics is a natural candidate
for heterogeneous computing since the computational bottleneck in these
simulations is the diagonalization of the Hamiltonian matrix, which is
performed several times during a single molecular dynamics trajectory. To
thoroughly test and understand the performance of our heterogeneous CPU+GPU
approach, we examine a variety of algorithmic implementations, benchmarks of
different hardware configurations, and applications of this methodology on
several large chemical and biological systems. Finally, to demonstrate the
capability of our implementation, we conclude with a large-scale DFTB MD
simulation of explicitly solvated HIV protease (3,974 atoms total) as a
proof-of-concept example of an extremely large/complex system which, to the
best of our knowledge, is the first time that an entire explicitly-solvated
protein has been treated at a quantum-based MD level of detail.
Speeding Up Molecular Dynamics Trajectory Analysis with MPI Parallelization